Early Identification of Skin Cancer Using Region Growing Technique and a Deep Learning Algorithm

Suhendro Y. Irianto; Rian Yunandar; M. S. Hasibuan; Deshinta Arrova Dewi; Nualyai Pitsachart

doi:10.28991/HIJ-2024-05-03-07

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Suhendro Y. Irianto
suhendro@darmajaya.ac.id
Department of Informatics, Institute Informatics and Business Darmajaya, Lampung 35144,, Indonesia
Rian Yunandar Department of Informatics, Institute Informatics and Business Darmajaya, Lampung 35144,, Indonesia
M. S. Hasibuan Department of Informatics, Institute Informatics and Business Darmajaya, Lampung 35144,, Indonesia
Deshinta Arrova Dewi Faculty of Data Science and Information Technology, INTI International University, Nilai,, Malaysia
Nualyai Pitsachart School of Nursing, Shinawatra University, 99 Moo 10, Bangtoey, Samkhok, Pathum Thani 12160,, Thailand

Vol. 5 No. 3 (2024): September

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Skin cancer, comprising both melanoma and non-melanoma forms, is a significant public health concern, constituting approximately 5.9% to 7.8% of annual cancer diagnoses. In Indonesia, the predominant form of the disease is basal cell carcinoma (65.5%), followed by squamous cell carcinoma (23%), and malignant melanoma (7.9%). Several studies have shown that early detection of its melanoma form is essential due to the heightened mortality risk. Therefore, this study aimed to assess the efficacy of the Region Growing + LSTM algorithm in improving detection accuracy compared to LSTM. The novelty of the study lay in addressing the inefficiencies of manual dermoscopy image examination and introducing a novel combination of Region Growing segmentation and Deep Learning LSTM for enhanced detection precision. The results showed that the proposed model could identify segmented areas before classification and achieved 96.62% accuracy, outperforming LSTM's 84%. However, LSTM exhibited shorter training and prediction times (39.3 seconds and 3.2 seconds, respectively) compared to Region Growing + LSTM (17 minutes and 2 seconds for training, 3 minutes and 49 seconds for prediction). Although Region Growing + LSTM offered superior accuracy, it required more time than LSTM, showing potential trade-offs between accuracy and efficiency in skin cancer image detection.

Doi: 10.28991/HIJ-2024-05-03-07

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Irianto, S. Y., Yunandar, R., Hasibuan, M. S., Dewi, D. A., & Pitsachart, N. (2024). Early Identification of Skin Cancer Using Region Growing Technique and a Deep Learning Algorithm. HighTech and Innovation Journal, 5(3), 640–662. https://doi.org/10.28991/HIJ-2024-05-03-07

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Early Identification of Skin Cancer Using Region Growing Technique and a Deep Learning Algorithm

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Early Identification of Skin Cancer Using Region Growing Technique and a Deep Learning Algorithm

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Most Cited Articles

Towards Bayesian Quantification of Permeability in Micro-scale Porous Structures – The Database of Micro Networks

Physicochemical and Microstructural Characterization of Klias Peat, Lumadan POFA, and GGBFS for Geopolymer Based Soil Stabilization

Seismic Upgradation of RC Beams Strengthened with Externally Bonded Spent Catalyst Based Ferrocement Laminates

Temporal Trends of Rainfall and Temperature over Two Sub-Divisions of Western Ghats

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